SOTAVerified

Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

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Showing 3151–3200 of 6748 papers

TitleStatusHype
In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance—0
Identifiability of latent-variable and structural-equation models: from linear to nonlinear—0
Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series—0
Identification of brain states, transitions, and communities using functional MRI—0
Identification of Effective Connectivity Subregions—0
Identification of market trends with string and D2-brane maps—0
Identification of Phase-Locked Loop System From Its Experimental Time Series—0
Identification of Recurrent Patterns in the Activation of Brain Networks—0
Identification robust inference for moments based analysis of linear dynamic panel data models—0
Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis—0
Identifying Constant and Unique Relations by using Time-Series Text—0
Identifying Cover Songs Using Information-Theoretic Measures of Similarity—0
Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems—0
Asymmetric Distributions from Constrained Mixtures—0
Identifying Grey-box Thermal Models with Bayesian Neural Networks—0
Cross-Frequency Time Series Meta-Forecasting—0
Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI—0
Identifying On-road Scenarios Predictive of ADHD usingDriving Simulator Time Series Data—0
Identifying Pairs in Simulated Bio-Medical Time-Series—0
Identifying Patients at Risk of Major Adverse Cardiovascular Events Using Symbolic Mismatch—0
Identifying Predictive Causal Factors from News Streams—0
Identifying Seizure Onset Zone from the Causal Connectivity Inferred Using Directed Information—0
Cross-Modal Data Programming Enables Rapid Medical Machine Learning—0
Identifying the module structure of swarms using a new framework of network-based time series clustering—0
Identifying Topology of Power Distribution Networks Based on Smart Meter Data—0
Analysis of cyclical behavior in time series of stock market returns—0
Identity Recognition in Intelligent Cars with Behavioral Data and LSTM-ResNet Classifier—0
Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data—0
IIT-GAN: Irregular and Intermittent Time-series Synthesis with Generative Adversarial Networks—0
Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification—0
Empirical facts characterizing banking crises: an analysis via binary time series—0
Image Processing Tools for Financial Time Series Classification—0
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models—0
Empirical analysis of daily cash flow time series and its implications for forecasting—0
Cross-Recurrence Quantification Analysis of Categorical and Continuous Time Series: an R package—0
IMG-NILM: A Deep learning NILM approach using energy heatmaps—0
I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic—0
Impact analysis of recovery cases due to COVID19 using LSTM deep learning model—0
Impact of Data Normalization on Deep Neural Network for Time Series Forecasting—0
Impact of noise on a dynamical system: prediction and uncertainties from a swarm-optimized neural network—0
Emotion-Inspired Deep Structure (EiDS) for EEG Time Series Forecasting—0
Correlation recurrent units: A novel neural architecture for improving the predictive performance of time-series data—0
Implications of Mortality Displacement for Effect Modification and Selection Bias—0
Cryptocurrency Market Consolidation in 2020--2021—0
Importance attribution in neural networks by means of persistence landscapes of time series—0
Imposing Connectome-Derived Topology on an Echo State Network—0
Causal Digital Twin from Multi-channel IoT—0
Improved Dynamic Time Warping (DTW) Approach for Online Signature Verification—0
Improved FRQI on superconducting processors and its restrictions in the NISQ era—0
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified